Jeffrey McGregor is the CEO of Truepic, an enterprise leader of Visual Risk Intelligence in the AI era.
OpenAI CEO Sam Altman delivered a stark warning to financial leaders at a Federal Reserve conference: “I am very nervous that we have an … impending fraud crisis.” He cautioned that generative AI is rapidly lowering the barrier for bad actors to commit fraud at scale and called on institutions to urgently redesign outdated verification systems, many of which AI has already rendered obsolete.
From insurance and banking to real estate and credentialing, nearly every industry has undergone some form of digital transformation. Processes that once relied on traditional methods of physical interactions and paper trails have shifted to digital photos, videos and remote documentation.
With digital content becoming the necessary proxy for in-person transactions, businesses must rely on images and data throughout their workflows. Underwriting teams assess policyholder risk using uploaded pictures while credit bureaus remotely verify commercial entities based on emailed documents, and lenders then make funding decisions based on borrower-submitted applications. Every business decision depends on whether the authenticity of each visual and the attached data can be trusted.
Watch Out For These Five Types Of Image Fraud
To stay vigilant against visual risk, it is essential to understand the most common methods used by bad actors to commit fraud using digital content. Here are five common image fraud techniques that are used to target workflow and digital operations:
1. Synthetically Generated Media
This is AI-generated content, including images, audio and video, that often looks hyperrealistic enough to pass as authentic. This is a highly accessible and sophisticated form of image fraud that could produce a fabricated utility bill used to open a new account or convincing images of vehicle damage to support an insurance claim.
Red Flags: According to photoforensic expert Dr. Hany Farid, synthetic content may contain irregular shadows and vanishing points, texture distortions, unnatural proportions or the absence of verifiable authentication data. Check to see if it comes from an AI-generator that adheres to open standards such as the C2PA.
2. Geolocation Spoofing
This entails falsifying the geographic location associated with an image by altering location settings or coordinates to make it appear captured in a different place. This can be achieved through various methods, such as manipulating GPS coordinates directly on the native device or using geo-spoofing applications to alter geolocation.
Red Flags: If the metadata conflicts with known device patterns, the visual context doesn’t match the expected geography or the coordinates don’t align with the user’s timeline, these could indicate fraud. Mock location modes can be enabled within specific app settings, illustrating how easily location data can be changed.
3. Metadata Manipulation
Manipulating an image file’s embedded metadata, such as timestamps or authorship tags, can falsify its origin or context. This manipulation constitutes image fraud by creating false information, like a backdated date to fit a policy window or a property photo that an applicant claims to own.
Red Flags: Photos with unverified metadata should not be relied on to make business decisions. Cryptographic and tamper-evident seals of metadata increase the trustworthiness of digital content.
4. Pixel Editing Via Rebroadcast Attack
Pixel editing refers to altering an image or video and then disguising the edit by photographing or recording the edited version from a screen or printout. This makes the content appear new and camera-original rather than a modified rebroadcast. For example, someone might display a doctored damage photo on a monitor and then re-capture it with another phone to make it look authentic.
Red Flags: Watch for duplicate pixel patterns, “picture-of-a-picture” images recaptured from screens or paper, unusually low variation across visuals or files that exactly match older submissions.
5. Object Reuse
Object reuse is done by recycling the same photo across multiple submissions, often with minor crops or edits to evade detection and create misleading visuals. Images can be easily altered, reused, stripped of metadata or edited by AI. For industries that rely on accurate visual documentation, such as assessing vehicle parts and determining eligibility for auto warranties, this is a fundamental risk.
Red Flags: Repeating objects or defects across unrelated images, signs of cloning and matches found through reverse image search or historical image sets might indicate object reuse.
How Leaders Can Respond To Visual Risk
Understanding how image fraud works is the first step. The next critical step leaders can take is to implement a verification process that protects digital workflows. There are several approaches business leaders can take to address this growing challenge and mitigate risk:
• Manual Validation: On-site assessments can provide strong accuracy and confidence, particularly in high-stakes scenarios. However, this approach can also be resource-intensive, time-consuming and difficult to scale across large operations.
• Stakeholder Training: Training employees to spot suspicious metadata or visual anomalies increases organizational awareness and builds resilience. At the same time, outcomes may vary depending on individual skill levels, and human error can still occur.
• Image And Data Authentication: Secure verification at the source helps ensure content integrity before it enters digital workflows, offering efficiency and scalability. That said, this often requires investment in technology and integration with existing systems.
When choosing the best path forward, weigh the operational resources that can be committed, the importance of fraud mitigation in each workflow and the trade-offs between speed and accuracy. A hybrid model can be effective by using on-site validation for when physical presence is required, training staff to help surface early warnings and embedding image and data authentication into routine processes to manage volume.
In today’s AI-driven world, where synthetic media is easy to create, understanding and mitigating visual risk becomes a strategic capability that gives businesses a competitive edge without compromising integrity.
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